Forecasting Covid-19 Dynamics in Brazil: A Data Driven Approach

Igor Gadelha Pereira, Joris Michel Guerin, Andouglas Gonçalves Silva Júnior, Gabriel Santos Garcia, Prisco Piscitelli, Alessandro Miani, Cosimo Distante and Luiz Marcos Garcia Gonçalves
Additional contact information
Igor Gadelha Pereira: Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil
Joris Michel Guerin: Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil
Andouglas Gonçalves Silva Júnior: Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil
Gabriel Santos Garcia: Institute of Biological Sciences, University of Brasilia, Distrito Federal 70910-900, Brazil
Prisco Piscitelli: Euro Mediterranean Scientific Biomedical Institute (ISBEM), 1040 Bruxelles, Belgium
Alessandro Miani: Department of Environmental Sciences and Policy, University of Milan, 20133 Milan, Italy
Cosimo Distante: Institute of Applied Sciences and Intelligent Systems, 73100 Lecce, Italy
Luiz Marcos Garcia Gonçalves: Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil

IJERPH, 2020, vol. 17, issue 14, 1-26

Abstract: The contribution of this paper is twofold. First, a new data driven approach for predicting the Covid-19 pandemic dynamics is introduced. The second contribution consists in reporting and discussing the results that were obtained with this approach for the Brazilian states, with predictions starting as of 4 May 2020. As a preliminary study, we first used an Long Short Term Memory for Data Training-SAE (LSTM-SAE) network model. Although this first approach led to somewhat disappointing results, it served as a good baseline for testing other ANN types. Subsequently, in order to identify relevant countries and regions to be used for training ANN models, we conduct a clustering of the world’s regions where the pandemic is at an advanced stage. This clustering is based on manually engineered features representing a country’s response to the early spread of the pandemic, and the different clusters obtained are used to select the relevant countries for training the models. The final models retained are Modified Auto-Encoder networks, that are trained on these clusters and learn to predict future data for Brazilian states. These predictions are used to estimate important statistics about the disease, such as peaks and number of confirmed cases. Finally, curve fitting is carried out to find the distribution that best fits the outputs of the MAE, and to refine the estimates of the peaks of the pandemic. Predicted numbers reach a total of more than one million infected Brazilians, distributed among the different states, with São Paulo leading with about 150 thousand confirmed cases predicted. The results indicate that the pandemic is still growing in Brazil, with most states peaks of infection estimated in the second half of May 2020. The estimated end of the pandemics (97% of cases reaching an outcome) spread between June and the end of August 2020, depending on the states.

Keywords: time series prediction; Covid-19 pandemic; modified auto-encoder; data-driven (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1660-4601/17/14/5115/pdf (application/pdf)
https://www.mdpi.com/1660-4601/17/14/5115/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:17:y:2020:i:14:p:5115-:d:384964

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().